This paper focused on ultradian rhythms (a sleep cycle of approximately 60 to 120 minute) for personalizing sleep stage estimation, and proposed a personalized sleep stage estimation method that weights the results estimated by machine learning with the predicted ultradian rhythms. The ultradian rhythms are predicted by the body movement density which is correlated with ultradian rhythm. To investigate the effectiveness of the proposed method, this paper conducts human subjects experiment for eight subjects.Clinical relevance― The proposed method is compared with the results estimated by conventional ML, and the result of the proposed method is competitive with their conventional counterparts. This indicates that the ultradian rhythm has the potential for developing personalized sleep stage estimation.